Pub Date : 2024-12-11DOI: 10.1109/TMLCN.2024.3515913
Zhizhou He;Fabien Héliot;Yi Ma
Reconfigurable Intelligent Surfaces (RIS) can enhance system performance at the cost of increased complexity in multi-user MIMO systems. The beamforming options scale with the number of antennas at the base station/RIS. Existing methods for solving this problem tend to use computationally intensive iterative methods that are non-scalable for large RIS-aided MIMO systems. We propose here a novel self-supervised contrastive learning neural network (NN) architecture to optimize the sum spectral efficiency through joint active and passive beamforming design in multi-user RIS-aided MIMO systems. Our scheme utilizes contrastive learning to capture the channel features from augmented channel data and then can be trained to perform beamforming with only 1% of labeled data. The labels are derived through a closed-form optimization algorithm, leveraging a sequential fractional programming approach. Leveraging the proposed self-supervised design helps to greatly reduce the computational complexity during the training phase. Moreover, our proposed model can operate under various noise levels by using data augmentation methods while maintaining a robust out-of-distribution performance under various propagation environments and different signal-to-noise ratios (SNR)s. During training, our proposed network only needs 10% of labeled data to converge when compared to supervised learning. Our trained NN can then achieve performance which is only $~7%$ and $~2.5%$ away from mathematical upper bound and fully supervised learning, respectively, with far less computational complexity.
{"title":"Self-Supervised Contrastive Learning for Joint Active and Passive Beamforming in RIS-Assisted MU-MIMO Systems","authors":"Zhizhou He;Fabien Héliot;Yi Ma","doi":"10.1109/TMLCN.2024.3515913","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3515913","url":null,"abstract":"Reconfigurable Intelligent Surfaces (RIS) can enhance system performance at the cost of increased complexity in multi-user MIMO systems. The beamforming options scale with the number of antennas at the base station/RIS. Existing methods for solving this problem tend to use computationally intensive iterative methods that are non-scalable for large RIS-aided MIMO systems. We propose here a novel self-supervised contrastive learning neural network (NN) architecture to optimize the sum spectral efficiency through joint active and passive beamforming design in multi-user RIS-aided MIMO systems. Our scheme utilizes contrastive learning to capture the channel features from augmented channel data and then can be trained to perform beamforming with only 1% of labeled data. The labels are derived through a closed-form optimization algorithm, leveraging a sequential fractional programming approach. Leveraging the proposed self-supervised design helps to greatly reduce the computational complexity during the training phase. Moreover, our proposed model can operate under various noise levels by using data augmentation methods while maintaining a robust out-of-distribution performance under various propagation environments and different signal-to-noise ratios (SNR)s. During training, our proposed network only needs 10% of labeled data to converge when compared to supervised learning. Our trained NN can then achieve performance which is only \u0000<inline-formula> <tex-math>$~7%$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>$~2.5%$ </tex-math></inline-formula>\u0000 away from mathematical upper bound and fully supervised learning, respectively, with far less computational complexity.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"147-162"},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10793234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-11DOI: 10.1109/TMLCN.2024.3500756
{"title":"IEEE Communications Society Board of Governors","authors":"","doi":"10.1109/TMLCN.2024.3500756","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3500756","url":null,"abstract":"","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10792973","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-09DOI: 10.1109/TMLCN.2024.3513267
Dheeraj Raja Kumar;Carles Antón-Haro;Xavier Mestre
Machine Learning tools are becoming very powerful alternatives to improve the robustness of wireless communication systems. Signal processing procedures that tend to collapse in the presence of model mismatches can be effectively improved and made robust by incorporating the selective use of data-driven techniques. This paper explores the use of neural network (NN)-based receivers to improve the reception of a Rate Splitting Multiple Access (RSMA) system. The intention is to explore several alternatives to conventional successive interference cancellation (SIC) techniques, which are known to be ineffective in the presence of channel state information (CSI) and model errors. The focus is on NN-based architectures that do not need to be retrained at each channel realization. The main idea is to replace some of the basic operations in a conventional multi-antenna SIC receiver by their NN-based equivalents, following a hybrid Model/Data-driven based approach that preserves the main procedures in the model-based signal demodulation chain. Three different architectures are explored along with their performance and computational complexity, characterized under different degrees of model uncertainty, including imperfect channel state information and non-linear channels. We evaluate the performance of data-driven architectures in overloaded scenario to analyze its effectiveness against conventional benchmarks. The study dictates that a higher degree of robustness of transceiver can be achieved, provided the neural architecture is well-designed and fed with the right information.
{"title":"Deep Receiver Architectures for Robust MIMO Rate Splitting Multiple Access","authors":"Dheeraj Raja Kumar;Carles Antón-Haro;Xavier Mestre","doi":"10.1109/TMLCN.2024.3513267","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3513267","url":null,"abstract":"Machine Learning tools are becoming very powerful alternatives to improve the robustness of wireless communication systems. Signal processing procedures that tend to collapse in the presence of model mismatches can be effectively improved and made robust by incorporating the selective use of data-driven techniques. This paper explores the use of neural network (NN)-based receivers to improve the reception of a Rate Splitting Multiple Access (RSMA) system. The intention is to explore several alternatives to conventional successive interference cancellation (SIC) techniques, which are known to be ineffective in the presence of channel state information (CSI) and model errors. The focus is on NN-based architectures that do not need to be retrained at each channel realization. The main idea is to replace some of the basic operations in a conventional multi-antenna SIC receiver by their NN-based equivalents, following a hybrid Model/Data-driven based approach that preserves the main procedures in the model-based signal demodulation chain. Three different architectures are explored along with their performance and computational complexity, characterized under different degrees of model uncertainty, including imperfect channel state information and non-linear channels. We evaluate the performance of data-driven architectures in overloaded scenario to analyze its effectiveness against conventional benchmarks. The study dictates that a higher degree of robustness of transceiver can be achieved, provided the neural architecture is well-designed and fed with the right information.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"45-63"},"PeriodicalIF":0.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10781451","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-04DOI: 10.1109/TMLCN.2024.3511475
Chenyuan Feng;Ahmed Arafa;Zihan Chen;Mingxiong Zhao;Tony Q. S. Quek;Howard H. Yang
This paper studies the efficiency of training a statistical model among an edge server and multiple clients via Federated Learning (FL) – a machine learning method that preserves data privacy in the training process – over wireless networks. Due to unreliable wireless channels and constrained communication resources, the server can only choose a handful of clients for parameter updates during each communication round. To address this issue, analytical expressions are derived to characterize the FL convergence rate, accounting for key features from both communication and algorithmic aspects, including transmission reliability, scheduling policies, and momentum method. First, the analysis reveals that either delicately designed user scheduling policies or expanding higher bandwidth to accommodate more clients in each communication round can expedite model training in networks with reliable connections. However, these methods become ineffective when the connection is erratic. Second, it has been verified that incorporating the momentum method into the model training algorithm accelerates the rate of convergence and provides greater resilience against transmission failures. Last, extensive empirical simulations are provided to verify these theoretical discoveries and enhancements in performance.
{"title":"Toward Understanding Federated Learning over Unreliable Networks","authors":"Chenyuan Feng;Ahmed Arafa;Zihan Chen;Mingxiong Zhao;Tony Q. S. Quek;Howard H. Yang","doi":"10.1109/TMLCN.2024.3511475","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3511475","url":null,"abstract":"This paper studies the efficiency of training a statistical model among an edge server and multiple clients via Federated Learning (FL) – a machine learning method that preserves data privacy in the training process – over wireless networks. Due to unreliable wireless channels and constrained communication resources, the server can only choose a handful of clients for parameter updates during each communication round. To address this issue, analytical expressions are derived to characterize the FL convergence rate, accounting for key features from both communication and algorithmic aspects, including transmission reliability, scheduling policies, and momentum method. First, the analysis reveals that either delicately designed user scheduling policies or expanding higher bandwidth to accommodate more clients in each communication round can expedite model training in networks with reliable connections. However, these methods become ineffective when the connection is erratic. Second, it has been verified that incorporating the momentum method into the model training algorithm accelerates the rate of convergence and provides greater resilience against transmission failures. Last, extensive empirical simulations are provided to verify these theoretical discoveries and enhancements in performance.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"80-97"},"PeriodicalIF":0.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10777576","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-26DOI: 10.1109/TMLCN.2024.3506874
Feng Shu;Baihua Shi;Yiwen Chen;Jiatong Bai;Yifan Li;Tingting Liu;Zhu Han;Xiaohu You
Massive multiple input multiple output (MIMO) antenna arrays eventuate a huge amount of circuit costs and computational complexity. To satisfy the needs of high precision and low cost in future green wireless communication, the conventional hybrid analog and digital MIMO receive structure emerges a natural choice. But it exists an issue of the phase ambiguity in direction of arrival (DOA) estimation and requires at least two time-slots to complete one-time DOA measurement with the first time-slot generating the set of candidate solutions and the second one to find a true direction by received beamforming over this set, which will lead to a low time-efficiency. To address this problem,a new heterogeneous sub-connected hybrid analog and digital ( $mathrm {H}^{2}$ AD) MIMO structure is proposed with an intrinsic ability of removing phase ambiguity, and then a corresponding new framework is developed to implement a rapid high-precision DOA estimation using only single time-slot. The proposed framework consists of two steps: 1) form a set of candidate solutions using existing methods like MUSIC; 2) find the class of the true solutions and compute the class mean. To infer the set of true solutions, we propose two new clustering methods: weight global minimum distance (WGMD) and weight local minimum distance (WLMD). Next, we also enhance two classic clustering methods: accelerating local weighted k-means (ALW-K-means) and improved density. Additionally, the corresponding closed-form expression of Cramer-Rao lower bound (CRLB) is derived. Simulation results show that the proposed frameworks using the above four clustering can approach the CRLB in almost all signal to noise ratio (SNR) regions except for extremely low SNR (SNR $lt -5$ dB). Four clustering methods have an accuracy decreasing order as follows: WGMD, improved DBSCAN, ALW-K-means and WLMD.
{"title":"A New Heterogeneous Hybrid Massive MIMO Receiver With an Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning","authors":"Feng Shu;Baihua Shi;Yiwen Chen;Jiatong Bai;Yifan Li;Tingting Liu;Zhu Han;Xiaohu You","doi":"10.1109/TMLCN.2024.3506874","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3506874","url":null,"abstract":"Massive multiple input multiple output (MIMO) antenna arrays eventuate a huge amount of circuit costs and computational complexity. To satisfy the needs of high precision and low cost in future green wireless communication, the conventional hybrid analog and digital MIMO receive structure emerges a natural choice. But it exists an issue of the phase ambiguity in direction of arrival (DOA) estimation and requires at least two time-slots to complete one-time DOA measurement with the first time-slot generating the set of candidate solutions and the second one to find a true direction by received beamforming over this set, which will lead to a low time-efficiency. To address this problem,a new heterogeneous sub-connected hybrid analog and digital (\u0000<inline-formula> <tex-math>$mathrm {H}^{2}$ </tex-math></inline-formula>\u0000AD) MIMO structure is proposed with an intrinsic ability of removing phase ambiguity, and then a corresponding new framework is developed to implement a rapid high-precision DOA estimation using only single time-slot. The proposed framework consists of two steps: 1) form a set of candidate solutions using existing methods like MUSIC; 2) find the class of the true solutions and compute the class mean. To infer the set of true solutions, we propose two new clustering methods: weight global minimum distance (WGMD) and weight local minimum distance (WLMD). Next, we also enhance two classic clustering methods: accelerating local weighted k-means (ALW-K-means) and improved density. Additionally, the corresponding closed-form expression of Cramer-Rao lower bound (CRLB) is derived. Simulation results show that the proposed frameworks using the above four clustering can approach the CRLB in almost all signal to noise ratio (SNR) regions except for extremely low SNR (SNR \u0000<inline-formula> <tex-math>$lt -5$ </tex-math></inline-formula>\u0000 dB). Four clustering methods have an accuracy decreasing order as follows: WGMD, improved DBSCAN, ALW-K-means and WLMD.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"17-29"},"PeriodicalIF":0.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10767772","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-20DOI: 10.1109/TMLCN.2024.3503543
Houssem Sifaou;Osvaldo Simeone
In many wireless application scenarios, acquiring labeled data can be prohibitively costly, requiring complex optimization processes or measurement campaigns. Semi-supervised learning leverages unlabeled samples to augment the available dataset by assigning synthetic labels obtained via machine learning (ML)-based predictions. However, treating the synthetic labels as true labels may yield worse-performing models as compared to models trained using only labeled data. Inspired by the recently developed prediction-powered inference (PPI) framework, this work investigates how to leverage the synthetic labels produced by an ML model, while accounting for the inherent bias concerning true labels. To this end, we first review PPI and its recent extensions, namely tuned PPI and cross-prediction-powered inference (CPPI). Then, we introduce two novel variants of PPI. The first, referred to as tuned CPPI, provides CPPI with an additional degree of freedom in adapting to the quality of the ML-based labels. The second, meta-CPPI (MCPPI), extends tuned CPPI via the joint optimization of the ML labeling models and of the parameters of interest. Finally, we showcase two applications of PPI-based techniques in wireless systems, namely beam alignment based on channel knowledge maps in millimeter-wave systems and received signal strength information-based indoor localization. Simulation results show the advantages of PPI-based techniques over conventional approaches that rely solely on labeled data or that apply standard pseudo-labeling strategies from semi-supervised learning. Furthermore, the proposed tuned CPPI method is observed to guarantee the best performance among all benchmark schemes, especially in the regime of limited labeled data.
{"title":"Semi-Supervised Learning via Cross-Prediction-Powered Inference for Wireless Systems","authors":"Houssem Sifaou;Osvaldo Simeone","doi":"10.1109/TMLCN.2024.3503543","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3503543","url":null,"abstract":"In many wireless application scenarios, acquiring labeled data can be prohibitively costly, requiring complex optimization processes or measurement campaigns. Semi-supervised learning leverages unlabeled samples to augment the available dataset by assigning synthetic labels obtained via machine learning (ML)-based predictions. However, treating the synthetic labels as true labels may yield worse-performing models as compared to models trained using only labeled data. Inspired by the recently developed prediction-powered inference (PPI) framework, this work investigates how to leverage the synthetic labels produced by an ML model, while accounting for the inherent bias concerning true labels. To this end, we first review PPI and its recent extensions, namely tuned PPI and cross-prediction-powered inference (CPPI). Then, we introduce two novel variants of PPI. The first, referred to as tuned CPPI, provides CPPI with an additional degree of freedom in adapting to the quality of the ML-based labels. The second, meta-CPPI (MCPPI), extends tuned CPPI via the joint optimization of the ML labeling models and of the parameters of interest. Finally, we showcase two applications of PPI-based techniques in wireless systems, namely beam alignment based on channel knowledge maps in millimeter-wave systems and received signal strength information-based indoor localization. Simulation results show the advantages of PPI-based techniques over conventional approaches that rely solely on labeled data or that apply standard pseudo-labeling strategies from semi-supervised learning. Furthermore, the proposed tuned CPPI method is observed to guarantee the best performance among all benchmark schemes, especially in the regime of limited labeled data.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"30-44"},"PeriodicalIF":0.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758826","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1109/TMLCN.2024.3502576
Tianjiao Sun;Sixing Yin;Li Deng;F. Richard Yu
Taking advantages of both unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs), UAV-mounted-RIS systems are expected to enhance transmission performance in complicated wireless environments. In this paper, we focus on system design for a UAV-mounted-RIS system and investigate joint optimization for the RIS’s phase shift and the UAV’s trajectory. To cope with the practical issue of inaccessible information on the user terminals’ (UTs) location and channel state, a reinforcement learning (RL)-based solution is proposed to find the optimal policy with finite steps of “trial-and-error”. As the action space is continuous, the deep deterministic policy gradient (DDPG) algorithm is applied to train the RL model. However, the online interaction between the agent and environment may lead to instability during the training and the assumption of (first-order) Markovian state transition could be impractical in real-world problems. Therefore, the decision transformer (DT) algorithm is employed as an alternative for RL model training to adapt to more general situations of state transition. Experimental results demonstrate that the proposed RL solutions are highly efficient in model training along with acceptable performance close to the benchmark, which relies on conventional optimization algorithms with the UT’s locations and channel parameters explicitly known beforehand.
{"title":"Reinforcement-Learning-Based Trajectory Design and Phase-Shift Control in UAV-Mounted-RIS Communications","authors":"Tianjiao Sun;Sixing Yin;Li Deng;F. Richard Yu","doi":"10.1109/TMLCN.2024.3502576","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3502576","url":null,"abstract":"Taking advantages of both unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs), UAV-mounted-RIS systems are expected to enhance transmission performance in complicated wireless environments. In this paper, we focus on system design for a UAV-mounted-RIS system and investigate joint optimization for the RIS’s phase shift and the UAV’s trajectory. To cope with the practical issue of inaccessible information on the user terminals’ (UTs) location and channel state, a reinforcement learning (RL)-based solution is proposed to find the optimal policy with finite steps of “trial-and-error”. As the action space is continuous, the deep deterministic policy gradient (DDPG) algorithm is applied to train the RL model. However, the online interaction between the agent and environment may lead to instability during the training and the assumption of (first-order) Markovian state transition could be impractical in real-world problems. Therefore, the decision transformer (DT) algorithm is employed as an alternative for RL model training to adapt to more general situations of state transition. Experimental results demonstrate that the proposed RL solutions are highly efficient in model training along with acceptable performance close to the benchmark, which relies on conventional optimization algorithms with the UT’s locations and channel parameters explicitly known beforehand.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"163-175"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758222","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-18DOI: 10.1109/TMLCN.2024.3501217
Rodrigo Carvalho;Faroq Al-Tam;Noélia Correia
As a growing trend, edge computing infrastructures are starting to be integrated with Internet of Things (IoT) systems to facilitate time-critical applications. These systems often require the processing of data with limited usefulness in time, so the edge becomes vital in the development of such reactive IoT applications with real-time requirements. Although different architectural designs will always have advantages and disadvantages, mobile gateways appear to be particularly relevant in enabling this integration with the edge, particularly in the context of wide area networks with occasional data generation. In these scenarios, mobility planning is necessary, as aspects of the technology need to be aligned with the temporal needs of an application. The nature of this planning problem makes cutting-edge deep reinforcement learning (DRL) techniques useful in solving pertinent issues, such as having to deal with multiple dimensions in the action space while aiming for optimum levels of system performance. This article presents a novel scalable DRL model that incorporates a pointer-network (Ptr-Net) and an actor-critic algorithm to handle complex action spaces. The model synchronously determines the gateway location and visit time. Ultimately, the gateways are able to attain high-quality trajectory planning with reduced latency.
{"title":"A2PC: Augmented Advantage Pointer-Critic Model for Low Latency on Mobile IoT With Edge Computing","authors":"Rodrigo Carvalho;Faroq Al-Tam;Noélia Correia","doi":"10.1109/TMLCN.2024.3501217","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3501217","url":null,"abstract":"As a growing trend, edge computing infrastructures are starting to be integrated with Internet of Things (IoT) systems to facilitate time-critical applications. These systems often require the processing of data with limited usefulness in time, so the edge becomes vital in the development of such reactive IoT applications with real-time requirements. Although different architectural designs will always have advantages and disadvantages, mobile gateways appear to be particularly relevant in enabling this integration with the edge, particularly in the context of wide area networks with occasional data generation. In these scenarios, mobility planning is necessary, as aspects of the technology need to be aligned with the temporal needs of an application. The nature of this planning problem makes cutting-edge deep reinforcement learning (DRL) techniques useful in solving pertinent issues, such as having to deal with multiple dimensions in the action space while aiming for optimum levels of system performance. This article presents a novel scalable DRL model that incorporates a pointer-network (Ptr-Net) and an actor-critic algorithm to handle complex action spaces. The model synchronously determines the gateway location and visit time. Ultimately, the gateways are able to attain high-quality trajectory planning with reduced latency.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10755120","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-04DOI: 10.1109/TMLCN.2024.3491054
Zain Ali;Zouheir Rezki;Mohamed-Slim Alouini
The next generation of communication devices will require robust connectivity for millions of ground devices such as sensors or mobile devices in remote or disaster-stricken areas to be connected to the network. Non-terrestrial network (NTN) nodes can play a vital role in fulfilling these requirements. Specifically, low-earth orbiting (LEO) satellites have emerged as an efficient and cost-effective technique to connect devices over long distances through space. However, due to their low power and environmental limitations, LEO satellites may require assistance from aerial devices such as high-altitude platforms (HAPs) or unmanned aerial vehicles to forward their data to the ground devices. Moreover, the limited power available at the NTNs makes it crucial to utilize available resources efficiently. In this paper, we present a model in which a LEO satellite communicates with multiple ground devices with the help of HAPs that relay LEO data to the ground devices. We formulate the problem of optimizing power allocation at the LEO satellite and all the HAPs to maximize the sum-rate of the system. To take advantage of the benefits of free-space optical (FSO) communication in satellites, we consider the LEO transmitting data to the HAPs on FSO links, which are then broadcast to the connected ground devices on radio frequency channels. We transform the complex non-convex problem into a convex form and compute the Karush-Kuhn-Tucker (KKT) conditions-based solution of the problem for power allocation at the LEO satellite and HAPs. Then, to reduce computation time, we propose a soft actor-critic (SAC) reinforcement learning (RL) framework that provides the solution in significantly less time while delivering comparable performance to the KKT scheme. Our simulation results demonstrate that the proposed solutions provide excellent performance and are scalable to any number of HAPs and ground devices in the system.
{"title":"Optimizing Power Allocation in HAPs Assisted LEO Satellite Communications","authors":"Zain Ali;Zouheir Rezki;Mohamed-Slim Alouini","doi":"10.1109/TMLCN.2024.3491054","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3491054","url":null,"abstract":"The next generation of communication devices will require robust connectivity for millions of ground devices such as sensors or mobile devices in remote or disaster-stricken areas to be connected to the network. Non-terrestrial network (NTN) nodes can play a vital role in fulfilling these requirements. Specifically, low-earth orbiting (LEO) satellites have emerged as an efficient and cost-effective technique to connect devices over long distances through space. However, due to their low power and environmental limitations, LEO satellites may require assistance from aerial devices such as high-altitude platforms (HAPs) or unmanned aerial vehicles to forward their data to the ground devices. Moreover, the limited power available at the NTNs makes it crucial to utilize available resources efficiently. In this paper, we present a model in which a LEO satellite communicates with multiple ground devices with the help of HAPs that relay LEO data to the ground devices. We formulate the problem of optimizing power allocation at the LEO satellite and all the HAPs to maximize the sum-rate of the system. To take advantage of the benefits of free-space optical (FSO) communication in satellites, we consider the LEO transmitting data to the HAPs on FSO links, which are then broadcast to the connected ground devices on radio frequency channels. We transform the complex non-convex problem into a convex form and compute the Karush-Kuhn-Tucker (KKT) conditions-based solution of the problem for power allocation at the LEO satellite and HAPs. Then, to reduce computation time, we propose a soft actor-critic (SAC) reinforcement learning (RL) framework that provides the solution in significantly less time while delivering comparable performance to the KKT scheme. Our simulation results demonstrate that the proposed solutions provide excellent performance and are scalable to any number of HAPs and ground devices in the system.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1661-1677"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10741546","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integration of high-precision cellular localization and machine learning (ML) is considered a cornerstone technique in future cellular navigation systems, offering unparalleled accuracy and functionality. This study focuses on localization based on uplink channel measurements in a fifth-generation (5G) new radio (NR) system. An attention-aided ML-based single-snapshot localization pipeline is presented, which consists of several cascaded blocks, namely a signal processing block, an attention-aided block, and an uncertainty estimation block. Specifically, the signal processing block generates an impulse response beam matrix for all beams. The attention-aided block trains on the channel impulse responses using an attention-aided network, which captures the correlation between impulse responses for different beams. The uncertainty estimation block predicts the probability density function of the user equipment (UE) position, thereby also indicating the confidence level of the localization result. Two representative uncertainty estimation techniques, the negative log-likelihood and the regression-by-classification techniques, are applied and compared. Furthermore, for dynamic measurements with multiple snapshots available, we combine the proposed pipeline with a Kalman filter to enhance localization accuracy. To evaluate our approach, we extract channel impulse responses for different beams from a commercial base station. The outdoor measurement campaign covers Line-of-Sight (LoS), Non Line-of-Sight (NLoS), and a mix of LoS and NLoS scenarios. The results show that sub-meter localization accuracy can be achieved.
高精度蜂窝定位与机器学习（ML）的集成被认为是未来蜂窝导航系统的基石技术，可提供无与伦比的精度和功能。本研究的重点是第五代（5G）新无线电（NR）系统中基于上行链路信道测量的定位。本文介绍了一种基于注意力辅助 ML 的单快照定位流水线，它由几个级联块组成，即信号处理块、注意力辅助块和不确定性估计块。具体来说，信号处理模块为所有波束生成脉冲响应波束矩阵。注意力辅助块利用注意力辅助网络对信道脉冲响应进行训练，从而捕捉不同波束脉冲响应之间的相关性。不确定性估计模块预测用户设备（UE）位置的概率密度函数，从而显示定位结果的置信度。应用了两种具有代表性的不确定性估计技术，即负对数概率和分类回归技术，并进行了比较。此外，对于具有多个可用快照的动态测量，我们将提议的管道与卡尔曼滤波器相结合，以提高定位精度。为了评估我们的方法，我们从一个商用基站提取了不同波束的信道脉冲响应。室外测量活动涵盖了视距（LoS）、非视距（NLoS）以及 LoS 和 NLoS 场景的混合。结果表明，可以实现亚米级定位精度。
{"title":"Attention-Aided Outdoor Localization in Commercial 5G NR Systems","authors":"Guoda Tian;Dino Pjanić;Xuesong Cai;Bo Bernhardsson;Fredrik Tufvesson","doi":"10.1109/TMLCN.2024.3490496","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3490496","url":null,"abstract":"The integration of high-precision cellular localization and machine learning (ML) is considered a cornerstone technique in future cellular navigation systems, offering unparalleled accuracy and functionality. This study focuses on localization based on uplink channel measurements in a fifth-generation (5G) new radio (NR) system. An attention-aided ML-based single-snapshot localization pipeline is presented, which consists of several cascaded blocks, namely a signal processing block, an attention-aided block, and an uncertainty estimation block. Specifically, the signal processing block generates an impulse response beam matrix for all beams. The attention-aided block trains on the channel impulse responses using an attention-aided network, which captures the correlation between impulse responses for different beams. The uncertainty estimation block predicts the probability density function of the user equipment (UE) position, thereby also indicating the confidence level of the localization result. Two representative uncertainty estimation techniques, the negative log-likelihood and the regression-by-classification techniques, are applied and compared. Furthermore, for dynamic measurements with multiple snapshots available, we combine the proposed pipeline with a Kalman filter to enhance localization accuracy. To evaluate our approach, we extract channel impulse responses for different beams from a commercial base station. The outdoor measurement campaign covers Line-of-Sight (LoS), Non Line-of-Sight (NLoS), and a mix of LoS and NLoS scenarios. The results show that sub-meter localization accuracy can be achieved.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1678-1692"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10741343","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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